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                    <h1>参加kaggle竞赛是怎样一种体验？</h1>
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                        				<div class="content-image"><img src="./参加kaggle竞赛是怎样一种体验？ - Data Application Lab_files/cover.png" alt="参加kaggle竞赛是怎样一种体验？" title="参加kaggle竞赛是怎样一种体验？"></div>
		<h2 class="single-content-title">参加kaggle竞赛是怎样一种体验？</h2><div class="single-post-content-text content-pad">
	<p style="text-align: right;"><span style="color: #999999;">&nbsp;作者：Naiyan Wang</span></p>
<p style="text-align: right;"><span style="color: #999999;">链接：https://www.zhihu.com/question/24533374/answer/34631808</span><br>
<span style="color: #999999;">来源：知乎</span><br>
<span style="color: #999999;">著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。</span></p>
<p style="text-align: left;">先贴下Kaggle Profile以示诚意：<a class=" wrap external" target="_blank" rel="nofollow noreferrer noopener">Winsty | Kaggle</a>（https://www.kaggle.com/40312-winsty）。</p>
<p>我是KDD Cup专业户，虽然每年做的都不是特别好。和一些Kaggle专业户们无论从数量还是质量上都差了好多。不过我一直觉得Kaggle在我PhD期间扮演了一个非常重要的角色。</p>
<p>下面列几个我觉得比较重要的问题自问自答下<strong>：</strong></p>
<h4><span style="color: #000000;"><strong>Kaggle的比赛究竟锻炼的是什么能力？</strong></span></h4>
<p>首先说，绝大部分的Kaggle比赛是<span style="color: #0000ff;">Data Mining(DM)比赛</span>，少数是和Discrete Optimization还有Computer Vision(CV) 有关。最重要的是和<span style="color: #0000ff;">Machine Learning(ML)</span>关系不大。<strong>这是很多人一个误区</strong>：很多人往往希望在Kaggle上学到很多ML的知识。</p>
<p>Kaggle教给我的第一件事情，就是让我清晰领会到了这两者之间的不同：<span style="color: #ff6600;"><strong>ML一般从模型或者算法出发</strong></span>，讲的是模型或者算法本身存在的不合理的地方，然后提出新的假设，从而去优化模型或算法。在这个过程中并不针对某一个具体的特殊的问题。而<span style="color: #ff6600;"><strong>DM恰恰相反，它是从数据本身问题本身出发</strong>，</span>希望针对问题的特性来设计最适合的方案。关键是在于对问题和数据的理解。之前总是觉得，DM/CV的paper都好水，到处找一个应用套。在我想明白这个问题之后，我就开始懂得欣赏DM/CV的paper。</p>
<p>其次，我觉得在一个DM的比赛中，最能锻炼到的是对于数据的”<span style="color: #008080;"><strong>嗅觉</strong></span>“。</p>
<p>举一个最有趣的例子，往往在比赛中会存在<span style="color: #008080;"><strong>Data Leakage</strong></span>的情况，也就是说，某些和label相关的信息不小心会泄漏在feature中。有人通过这样的feature取得了很好的成绩之后，往往有人觉得非常鄙视。当然我不是说Data Leakage是一件好事情，但是在这背后往往隐藏的是<span style="color: #008080;"><strong>发现leakage的人对于数据本身深刻的认识</strong>。</span>这并不是每个人都能做到的。换句话讲，就算没有leakage，这群人还是会排名很前。在Kaggle的比赛中，能收获最大的就是这种嗅觉。这其实也把我自己训练成了一个<span style="color: #008080;"><strong>data believer</strong></span>：也许一次两次的巧合真的是意外，但是如果巧合总是持续发生，那么背后一定有一个原因。</p>
<p><img loading="lazy" class="aligncenter size-medium wp-image-5030" src="./参加kaggle竞赛是怎样一种体验？ - Data Application Lab_files/welcom-300x139.png" alt="" width="300" height="139" srcset="https://www.dataapplab.com/wp-content/uploads/2017/03/welcom-300x139.png 300w, https://www.dataapplab.com/wp-content/uploads/2017/03/welcom-600x278.png 600w, https://www.dataapplab.com/wp-content/uploads/2017/03/welcom-768x356.png 768w, https://www.dataapplab.com/wp-content/uploads/2017/03/welcom-1024x474.png 1024w, https://www.dataapplab.com/wp-content/uploads/2017/03/welcom-255x118.png 255w" sizes="(max-width: 300px) 100vw, 300px"></p>
<h4><strong>怎样才能做好Kaggle的比赛？</strong></h4>
<p><span style="color: #3366ff;">第一点也是最重要的一点就是专注，专注，再专注。</span>其实说来惭愧，我在这点上做得很不好。<br>
第一年开始高歌猛进了一段，中间卡住，也是能力不足，然后就放弃了。<br>
第二年抱学长大腿侥幸成绩不错，其实个人来讲没有做出太大贡献。先是去写了一个NIPS，然后又去处理了一些私事。<br>
第三年做到一半，很偶然地被拉去百度做了ImageNet的比赛，精力主要就放到了ImageNet上，坑了队友。。。<br>
所以其实这三年，离我自己的期待都有一定的距离，我也很清楚问题出在哪里。希望明年能真正focus一次吧。</p>
<p><span style="color: #3366ff;">第二点，永远不要放弃</span>。希望总存在于绝望之后。每个比赛都会有一个瓶颈期。耐心地去突破它后，就是一片开阔天空。</p>
<p><span style="color: #3366ff;">第三点，切记只看不做。</span>很多人只喜欢纸上谈兵，武断觉得这个问题，或者这个数据就应该是怎样。很多数据的特质都是要真正动手做进去才能发现其中的奥妙，针对这些特质设计的一些Feature或者Model，往往都能带来极大的提高。</p>
<p><span style="color: #3366ff;">第四点，才是多看，尤其是在比赛结束之后。</span>很多leader会在比赛结束之后部分甚至全部地公布自己的解法。这个时候返回去看看在比赛中自己忽略掉了什么地方，才是成长最最重要的。第一年的比赛教给了我在一个<span style="color: #ff6600;"><strong>实际的推荐系统里session的重要性</strong></span>。第二年的比赛教给了我<span style="color: #ff6600;"><strong>机器不是万能的，人肉一些规则往往更有效</strong></span>。<span style="color: #ff0000;"><span style="color: #008000;">每一年其实都对实际的Data Mining问题有新的认识，也更清楚了哪些paper是真的work，哪些是在灌水。这其实也就是我来做比赛的最大目的吧。</span></span></p>
<p><img loading="lazy" class="aligncenter size-medium wp-image-5031" src="./参加kaggle竞赛是怎样一种体验？ - Data Application Lab_files/database-300x150.png" alt="" width="300" height="150" srcset="https://www.dataapplab.com/wp-content/uploads/2017/03/database-300x150.png 300w, https://www.dataapplab.com/wp-content/uploads/2017/03/database-600x300.png 600w, https://www.dataapplab.com/wp-content/uploads/2017/03/database-768x384.png 768w, https://www.dataapplab.com/wp-content/uploads/2017/03/database-1024x511.png 1024w, https://www.dataapplab.com/wp-content/uploads/2017/03/database-255x127.png 255w" sizes="(max-width: 300px) 100vw, 300px"></p>
<h4><strong>技术方面上什么最关键？</strong></h4>
<p>前面提到Kaggle主要是以<span style="color: #ff0000;"><strong>Data Mining</strong></span>的比赛为主，那么这个答案就非常直接了：<span style="color: #ff0000;"><a style="color: inherit; text-decoration: inherit;" href="https://my-drugs-blog.com/">my-drugs-blog.com</a></span> 无数经验告诉我们，做Kaggle比赛如果是抱着Machine Learning的态度，沉迷于facny的模型而忽略数据本身，一定会死得很惨很惨！</p>
<p>当然，基本的ML知识还是有的。在比赛中，最常用的分类器一般是Gradient Boosting Tree(GBDT)和Random Forest（对，你没看错，不是那个在教科书中推Dual时让很多人痛不欲生的SVM）一些常见的预处理技巧，比如PCA，KMeans，TF/IDF，Hashing等等都还是必须的。这里就不展开讲了。</p>
<p>最后，但是非常关键的一点是Ensemble 从KDD Cup到Imagenet，我从来没见过<span style="color: #ff0000;">ensemble</span>不会改善结果的情况，只是多与少的问题。不做ensemble就意味着你自己告别了一大块宝藏。</p>
<p><img loading="lazy" class="aligncenter size-medium wp-image-5032" src="./参加kaggle竞赛是怎样一种体验？ - Data Application Lab_files/kernel-300x145.png" alt="" width="300" height="145" srcset="https://www.dataapplab.com/wp-content/uploads/2017/03/kernel-300x145.png 300w, https://www.dataapplab.com/wp-content/uploads/2017/03/kernel-600x289.png 600w, https://www.dataapplab.com/wp-content/uploads/2017/03/kernel-768x370.png 768w, https://www.dataapplab.com/wp-content/uploads/2017/03/kernel-1024x493.png 1024w, https://www.dataapplab.com/wp-content/uploads/2017/03/kernel-255x123.png 255w" sizes="(max-width: 300px) 100vw, 300px"></p>
<h4><strong>总结</strong></h4>
<p><span style="color: #ff0000;"><strong>我觉得Kaggle是一个对于每个想成为所谓的Data Scientist的同学最好的试炼厂</strong></span>。在这里，你就会知道课上学到的那些东西和能解决一个实际问题所需要的能力的差距。更何况，在学校里往往是拿不到任何大规模的数据。绝大多数课堂上用的还是只有几百个几千个数据的UCI dataset。Kaggle是缩小这个gap最好的一个地方。</p>
<p>最后，希望大家在Kaggle上都玩得愉快~~</p>
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